The bias-based metrics evaluate how biased the model is for specific expressions or profanities easily assumed to be hateful. HateXplain follows AUC-based metrics developed by [1]}. The model classifies the data into ‘toxic’–hateful and offensive–and ‘non-toxic’–normal. For evaluating the model’s prediction results, the data are separated into four subsets: \(D_{g}^{+}, D_{g}^{-}\) , \(D^{+}\) , and \(D^{-}\) . The target group labels are considered standard for dividing data into subgroups. The notations with \(g\)  denote the data of a specific subgroup among the subgroups, and the notations without g are the remaining data. \(+\)  and \(-\)  mean that the data are toxic and non-toxic, respectively. Based on these subsets, three AUC metrics are calculated.
